RAILwAI provides a cloud‑based platform that consolidates data from GMAO systems, maintenance forms, and onboard sensors into a single environment, then applies AI models to predict equipment degradation and optimize maintenance schedules. The solution offers API connectors, real‑time sensor ingestion, automated data cleaning, and a 360° dashboard, enabling railway infrastructure managers and operators to shift to condition‑based maintenance, reduce downtime, and control costs.
Funding
Funding not disclosed
Founders
Product
Problem
Railway operators and infrastructure managers face fragmented data from multiple maintenance systems, sensors, and legacy tools, resulting in underutilized information and time‑consuming analysis. This hampers the shift from scheduled preventive maintenance to condition‑based, predictive approaches, leading to higher costs and increased risk of failures.
Solution
RAILwAI offers a cloud‑based platform that aggregates data from diverse sources—including GMAO systems, proprietary maintenance forms, and onboard sensors—into a single, unified environment. The platform enriches and cross‑references these datasets, applying machine‑learning models to predict equipment degradation and optimize maintenance schedules. Users can monitor the impact of maintenance actions over time, generate actionable insights, and make data‑driven decisions to improve network reliability while controlling expenses. Integration is achieved through ready‑made APIs for major GMAO solutions (e.g., Carl, Maximo, SAP) and direct connections to leading sensor manufacturers, enabling seamless data flow without extensive custom development.
Target Audience
Primary customers are railway infrastructure managers, rail operating companies, engineering and construction firms involved in rail projects, and sensor manufacturers seeking to monetize and integrate their data streams.
Features
- API connectors for leading GMAO platforms and custom import tools for proprietary systems
- Real‑time ingestion of sensor data from manufacturers such as Konux, Sensonic, and VAPERAIL
- AI‑powered analytics that forecast failures, recommend condition‑based interventions, and quantify maintenance impact
- 360° dashboard providing a consolidated view of asset health, performance metrics, and cost savings
- Automated data cleaning and enrichment pipelines that reduce processing time by up to 90%
- Modular architecture allowing customization for infrastructure managers, railway operators, engineering firms, construction companies, and sensor vendors